Abstract: A method of data nibble-histogram compression can include determining a first amount of space freed by compressing the input data using a first compression technique, determining a second amount of space freed by compressing the input data using a second, different compression technique, compressing the input data using the compression technique of the first and second compression techniques determined to free up more space to create compressed input data, and inserting into the compressed input data, security data including one of a message authentication control (MAC) and an inventory control tag (ICT).

Abstract: Embodiments are directed to providing a secure address translation service.

Abstract: Specular light shadow removal is described for use in image de-noising. In one example a method includes placing a window on an image, determining a cumulative distribution function for the window, determining a destination histogram for the window, determining a cumulative distribution function for the destination histogram, replacing the intensity of a pixel with the smallest index for which the histogram distribution for the pixel is greater than the window distribution, repeating determining a cumulative distribution function, a destination histogram, and a cumulative distribution function and replacing the intensity for a plurality of windows of the image, and de-noising the image after repeating by applying a median filter to the image.

Abstract: A method of data nibble-histogram compression can include determining a first amount of space freed by compressing the input data using a first compression technique, determining a second amount of space freed by compressing the input data using a second, different compression technique, compressing the input data using the compression technique of the first and second compression techniques determined to free up more space to create compressed input data, and inserting into the compressed input data, security data including one of a message authentication control (MAC) and an inventory control tag (ICT).

Abstract: In one example, an apparatus for Advanced Encryption Standard (AES) substitutions box (S-box) encryption includes an S-Box logic function and a MixColumns multiplication operation. The S-box logic function takes as input a state and is an 8-bit to 8-bit logic function, and wherein the S-box logic function is minimized such that an S-box round comprises nine not-and (NAND) levels and duplications of a logical product of the minimized S-box logic function are eliminated. The MixColumns multiplication operation comprises a plurality of factors that are exclusive ORed (XOR) with an output of the S-box round to obtain a scaled 16-byte output.

Abstract: The present disclosure is directed to systems and methods for the secure transmission of plaintext data blocks encrypted using a NIST standard encryption to provide a plurality of ciphertext data blocks, and using the ciphertext data blocks to generate a Galois multiplication-based authentication tag and parity information that is communicated in parallel with the ciphertext blocks and provides a mechanism for error detection, location and correction for a single ciphertext data block or a plurality of ciphertext data blocks included on a storage device. The systems and methods include encrypting a plurality of plaintext blocks to provide a plurality of ciphertext blocks. The systems and methods include generating a Galois Message Authentication Code (GMAC) authentication tag and parity information using the ciphertext blocks.

Abstract: In one example an apparatus comprises a memory and a processor to create, from a first deep neural network (DNN) model, a first plurality of DNN models, generate a first set of adversarial examples that are misclassified by the first plurality of deep neural network (DNN) models, determine a first set of activation path differentials between the first plurality of adversarial examples, generate, from the first set of activation path differentials, at least one composite adversarial example which incorporates at least one intersecting critical path that is shared between at least two adversarial examples in the first set of adversarial examples, and use the at least one composite adversarial example to generate a set of inputs for a subsequent training iteration of the DNN model. Other examples may be described.

Abstract: The present disclosure is directed to systems and methods for the selective introduction of low-level pseudo-random noise into at least a portion of the weights used in a neural network model to increase the robustness of the neural network and provide a stochastic transformation defense against perturbation type attacks. Random number generation circuitry provides a plurality of pseudo-random values. Combiner circuitry combines the pseudo-random values with a defined number of least significant bits/digits in at least some of the weights used to provide a neural network model implemented by neural network circuitry. In some instances, selection circuitry selects pseudo-random values for combination with the network weights based on a defined pseudo-random value probability distribution.

Abstract: A flexible aes instruction set for a general purpose processor is provided. The instruction set includes instructions to perform a “one round” pass for aes encryption or decryption and also includes instructions to perform key generation. An immediate may be used to indicate round number and key size for key generation for 128/192/256 bit keys. The flexible aes instruction set enables full use of pipelining capabilities because it does not require tracking of implicit registers.

Abstract: A mechanism is described for facilitating multifunction perceptron-based machine learning in computing environments, according to one embodiment. A method of embodiments, as described herein, includes generating a multifunction perceptron architecture having a plurality of neurons to perform one or more neuron functions in a machine learning environment, wherein the plurality of neurons includes one or more of splitter neurons, mixer neurons, and counter neurons, wherein the plurality of neurons include heterogenous neurons.

Abstract: Logic may implement implicit integrity techniques to maintain integrity of data. Logic may perform operations on data stored in main memory, cache, flash, data storage, or any other memory. Logic may perform more than one pattern check to determine repetitions of entities within the data. Logic may determine entropy index values and/or Boolean values and/or may compare the results to threshold values to determine if a data unit is valid. Logic may merge a tag with the data unit without expanding the data unit to create an encoded data unit. Logic may decode and process the encoded data unit to determine the data unit and the tag. Logic may determine value histograms for two or more entities, determine a sum of repetitions of the two or more entities, and compare the sum to a threshold value. Logic may determine that a data unit is valid or is corrupted.

Abstract: Before sending a message to a destination device, a source device automatically uses a pattern matching algorithm to analyze entropy characteristics of a plaintext version of the message. The pattern matching algorithm uses at least one pattern matching test to generate at least one entropy metric for the message. The source device automatically determines whether the message has sufficiently low entropy, based on results of the pattern matching algorithm. In response to a determination that the message does not have sufficiently low entropy, the source device automatically generates integrity metadata for the message and sends the integrity metadata to the destination device. However, in response to a determination that the message has sufficiently low entropy, the source device sends the message to the destination device without sending any integrity metadata for the message to the destination device. Other embodiments are described and claimed.

Abstract: A method of data nibble-histogram compression can include determining a first amount of space freed by compressing the input data using a first compression technique, determining a second amount of space freed by compressing the input data using a second, different compression technique, compressing the input data using the compression technique of the first and second compression techniques determined to free up more space to create compressed input data, and inserting into the compressed input data, security data including one of a message authentication control (MAC) and an inventory control tag (ICT).

Abstract: Systems, apparatus and methods are described including operations for a dual mode GMM (Gaussian Mixture Model) scoring accelerator for both speech and video data.

Abstract: Median filtering of images is described using a directed search. In one example a method includes sliding a first window to a second position on an image to generate a second window where the first window overlaps the second window, determining a second histogram of pixel values by extracting a set of pixels from the first histogram and adding a set of pixels to the first histogram so that the second histogram has only pixels within the second window, determining a second median value of the pixel values using the second histogram by searching pixel values of the second histogram for the median starting at the median value of the first histogram, and repeating sliding the window determining a histogram and determining a median value until a complete median set of an area of interest of the image is determined.

Abstract: Specular light shadow removal is described for use in image de-noising. In one example a method includes placing a window on an image, determining a cumulative distribution function for the window, determining a destination histogram for the window, determining a cumulative distribution function for the destination histogram, replacing the intensity of a pixel with the smallest index for which the histogram distribution for the pixel is greater than the window distribution, repeating determining a cumulative distribution function, a destination histogram, and a cumulative distribution function and replacing the intensity for a plurality of windows of the image, and de-noising the image after repeating by applying a median filter to the image.

Abstract: Embodiments of a system and method for detecting a palm of a hand using an image are generally described herein. A method for detecting a palm of a hand may include determining a plurality of cumulative distance values from pixels in a set of image pixels to pixels in a plurality of finger template matches, wherein the set of image pixels includes a set of image pixels on detected edges in the image. The method may include selecting a set of finger template matches from the plurality of finger template matches, corresponding to a set of smallest cumulative distance values from the plurality of cumulative distance values. The method may include refining the set of finger template matches to create refined finger templates, bundling refined finger templates to create a bundle of refined finger templates, and verifying that the bundle of refined finger templates includes a valid palm detection.

Abstract: Embodiments of a system and methods for skin detection and pose determination of a hand in an image are generally described herein. A method for may include detecting skin pixels in an image using a multi-layer skin filter and classifying the skin pixels into a set of foreground skin pixels and a set of background skin pixels. The method may include storing information about the set of background skin pixels in a persistent memory. The method may include determining a set of features from the set of foreground skin pixels, and clustering features from the set of features to create a set of hand pose descriptors. The method may include determining a set of candidate hand pose region descriptors and a set of candidate hand pose contour descriptors that match the set of hand pose descriptors and detecting a valid hand pose using the sets of candidate descriptors.

Abstract: Embodiments of a system and method for detecting a palm of a hand using an image are generally described herein. A method for detecting a palm of a hand may include determining a plurality of cumulative distance values from pixels in a set of image pixels to pixels in a plurality of finger template matches, wherein the set of image pixels includes a set of image pixels on detected edges in the image. The method may include selecting a set of finger template matches from the plurality of finger template matches, corresponding to a set of smallest cumulative distance values from the plurality of cumulative distance values. The method may include refining the set of finger template matches to create refined finger templates, bundling refined finger templates to create a bundle of refined finger templates, and verifying that the bundle of refined finger templates includes a valid palm detection.

Abstract: Technologies for performing network analysis of a network include a network analytics node to determine one or more features of network traffic of the network. Each feature includes indexes associated with a link property, a protocol, and a time property. The network analytics node monitors the network traffic of the network based on the one or more features and generates one or more observation vectors. Each observation vector includes a plurality of the one or more features based on the monitored network traffic. The network analytics node performs a statistical network analysis of the network traffic based on the generated one or more observation vectors to generate a probabilistic model of the network traffic.